Text-dependent Speaker Recognition by Combination of LBG VQ and DTW for Persian Language

Bahaghighat, Mahdi; Sahba, Farshid; Tehrani, Ehsan Sobhani

doi:10.5120/8126-1711

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Data imbalance issues are common phenomena in various fields such as medical diagnosis, anomaly detection, economics, speaker recognition [48] and environmental concerns. They occur when the quantities of samples in datasets are not roughly equal.…”

Section: Model Evaluation and Resultsmentioning

confidence: 99%

Mobile User Indoor-Outdoor Detection through Physical Daily Activities

Kelishomi

Garmabaki

Bahaghighat³

et al. 2019

Sensors

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An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications.

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Section: Model Evaluation and Resultsmentioning

confidence: 99%

Mobile User Indoor-Outdoor Detection through Physical Daily Activities

Kelishomi

Garmabaki

Bahaghighat³

et al. 2019

Sensors

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show abstract

“…Today's data sciences, including Artificial Intelligence, Machine Learning, and Data Mining, are considered in widespread applications throughout the world [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Data mining is a special process that targets extracting valid, previously unknown, and ultimately comprehensible information from usually a large database in order to deploy in the decision-making systems.…”

Section: Related Workmentioning

confidence: 99%

Optimum Outlier Detection in Internet of Things Industries Using Autoencoder

Hajikarimi¹,

Bahaghighat

2021

Springer Tracts in Nature-Inspired Computing

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“…On the other hand, there are some approaches to predict BTC price based on Natural Language Processing (NLP) and sentiment analysis. Today, NLP as an AI (artificial intelligence) technology and Deep learning [9,2] are used together in advanced text mining/analytic tools [23,4,26,8,7]. These approaches get social media text data from Twitter, Facebook, and etc., as the input and try to draw a link between the content of daily messages and the BTC price.…”

Section: Introductionmentioning

confidence: 99%

Bitcoin daily close price prediction using optimized grid search method

Rostami¹,

Bahaghighat

Zanjireh

2021

Acta Universitatis Sapientiae, Informatica

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Cryptocurrencies are digital assets that can be stored and transferred electronically. Bitcoin (BTC) is one of the most popular cryptocurrencies that has attracted many attentions. The BTC price is considered as a high volatility time series with non-stationary and non-linear behavior. Therefore, the BTC price forecasting is a new, challenging, and open problem. In this research, we aim the predicting price using machine learning and statistical techniques. We deploy several robust approaches such as the Box-Jenkins, Autoregression (AR), Moving Average (MA), ARIMA, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and Grid Search algorithms to predict BTC price. To evaluate the performance of the proposed model, Forecast Error (FE), Mean Forecast Error (MFE), Mean Absolute Error (MAE), Mean Squared Error (MSE), as well as Root Mean Squared Error (RMSE), are considered in our study.

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Text-dependent Speaker Recognition by Combination of LBG VQ and DTW for Persian Language

Cited by 6 publications

References 20 publications

Mobile User Indoor-Outdoor Detection through Physical Daily Activities

Mobile User Indoor-Outdoor Detection through Physical Daily Activities

Optimum Outlier Detection in Internet of Things Industries Using Autoencoder

Bitcoin daily close price prediction using optimized grid search method

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